Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts.

BACKGROUND: Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits. OBJECTIVE: In this study, we analyzed the distinctive linguistic characteristics in users' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. METHODS: Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health-related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. RESULTS: We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. CONCLUSIONS: By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online.

Antioxidant activities of thermally treated Sesamum indicum L. leaf extracts and their inhibitory effects against growth and metastatic properties of human colon cancer cells.

The study aimed to investigate antioxidant activities of two different thermally treated sesame (Sesamum indicum L.) leaf ethanol extract, steamed sesame leaf extract (SSLE) and roasted sesame leaf extract (RSLE), and their inhibitory effects on uncontrolled growth and increased metastatic properties in human colon cancer cell lines. Both SSLE and RSLE contained pedaliin as the major polyphenol and its aglycon, pedalitin, as a minor component and exhibited radical scavenging activities and ferric reducing antioxidant power. SSLE and RSLE decreased growth of HT29 and HCT116 colon cancer cells, which was attributed to the induction of apoptosis and cell cycle arrest at either G2/M (by SSLE in HCT116) or S phase (by RSLE in HCT116). Furthermore, SSLE and RSLE inhibited migration and adhesion in both cell lines. These results indicate that thermally treated sesame leaves retained pedaliin content and exhibited antioxidant activities and inhibitory activities against the growth and metastatic properties of colon cancer cells.

Attitudes toward Alzheimer's disease and dementia caregiving and health outcomes: Racial and ethnic differences.

This study aimed to explore racial/ethnic differences in the attitudes toward Alzheimer's Disease (AD) and dementia caregiving among midlife women who were family caregivers of persons living with AD (MWPLAD) in the U.S. and examine the associations of the attitudes to their health outcomes. This was a cross-sectional online survey study among 172 MWPLAD. The instruments included: the Attitude toward AD and Related Dementias Scale, the Questions on Attitudes toward AD Caregiving, the Social Readjustment Rating Scale, the EQ-5D-5L and the Midlife Women's Symptom Index. Multiple linear regression analyses were conducted. There were significant racial/ethnic differences in caregivers' attitudes toward dementia caregiving, health-related quality of life, and total severity scores of symptoms (p < .01). Controlling for covariates including race/ethnicity, caregivers' positive attitudes toward dementia caregiving were significantly associated with their health outcomes (p ≤ .05). Interventions for MWPLAD need to consider racial/ethnical differences in their attitudes toward dementia caregiving.

A lexicon-based approach to examine depression detection in social media: the case of Twitter and university community.

Globally, the number of people who suffer from depression is consistently increasing. Because both detecting and addressing the early stage of depression is one of the strongest factors for effective treatment, a number of scholars have attempted to examine how to detect and address early-stage depression. Recent studies have been focusing on the use of social media for depression detection where users express their thoughts and emotions freely. With this trend, we examine two-step approaches for early-stage depression detection. First, we propose a depression post-classification model using multiple languages Twitter datasets (Korean, English, and Japanese) to improve the applicability of the proposed model. Moreover, we built a depression lexicon for each language, which mental health experts verified. Then, we applied the proposed model to a more specific user group dataset, a community of university students (Everytime), to examine whether the model can be employed to address depression posts in more specific user groups. The classification results present that the proposed model and approach can effectively detect depression posts of a general user group (Twitter), as well as specific user group datasets. Moreover, the implemented models and datasets are publicly available.

Computational analysis of a collaboration network on human-computer interaction in Korea.

Since information and communication technology (ICT) has become one of the leading and essential fields for allowing developing countries to have the major growth engines, the majority of the countries have promoted collaboration in every ICT-related topics. In this study, we performed the trend and collaboration network analysis (CNA) in Korea for 2010-2019 among researchers who are related to human-computer interaction, one of the hottest research areas in ICT. Publication data were collected from SciVal, and the collaboration network was determined using degree, closeness, betweenness centralities, and PageRank. Hence, key researchers were identified based on their centrality metrics. The dataset contained 7,155 publications, thus reflecting the contributions of a total of 243 authors. The results of our data analysis demonstrated that key researchers can be identified via CNA; this aspect was not evident from the results of the most productive researchers. Additionally, on the basis of the results, the implications and limitations of this study were analyzed.

In Vitro Antioxidant and Anti-Colon Cancer Activities of Sesamum indicum L. Leaf Extract and Its Major Component, Pedaliin.

Sesame (Sesamum indicum L.) leaves (SLs) are used as vegetables and traditional medicines in Asian and African countries. We investigated in vitro antioxidant and anti-colon cancer efficacy of ethanol extract of SL (SLE) and its major bioactive component. SLE contained appreciable amount of major classes of antioxidant phytochemicals, such as total polyphenols, total flavonoids, and carotenoids, and correspondingly exhibited antioxidant activities, such as radical scavenging activity and ferric reducing antioxidant power (FRAP). A cell viability assay showed that SLE time- and dose-dependently attenuated the growth of human colon cancer cells, HT29 and HCT116. Flow cytometry analysis showed that SLE increased sub-G1 (in HT29 and HCT116) and G2/M (in HCT116) cell populations, suggesting that the growth inhibition by SLE was due to induction of apoptosis and G2/M cell cycle arrest. Trans-well and wound-healing assays showed that SLE alleviated invasion and migration of HT29 and HCT116 cells in non-cytotoxic conditions. High-performance liquid chromatography analysis revealed that pedaliin (6-hydroxylueolin 7-methyl ether 6-glucoside; pedalitin-6-O-glucoside) was a major constituent of SLE. Moreover, FRAP, growth-inhibitory, anti-invasive, and anti-migratory activities of pedaliin were found. These results demonstrated that SLE possesses in vitro antioxidant and anti-colon cancer activities and that pedaliin is a major component contributing to such activities.

A ratiometric fluorescence sensor based on enzymatically activatable micellization of TPE derivatives for quantitative detection of alkaline phosphatase activity in serum.

A novel ratiometric fluorescence assay via enzymatically activatable micellization in aqueous solution was devised for quantitative detection of alkaline phosphatase (ALP) activity. We demonstrated that the dephosphorylation of the water-soluble, phosphate-functionalized, fluorophore monomer P-TPE-TG, induced by an enzymatic reaction of ALP, leads to micelle formation in aqueous solution because its water-soluble functionality is reduced. The dephosphorylation-induced micellization of P-TPE-TG exhibited a ratiometric sensing response for various ALP concentrations (10-200 mU mL-1) and provided a suitable sensing platform for naked eye detection with increased fluorescence quantum yield (Φ = 3.2%), even compared to a typical TPE-based sensor (Φ = 1.0%), where ALP can be sensed with a detection limit of 0.034 mU mL-1. In addition, P-TPE-TG displayed excellent sensing performance at concentrations from 0 to 50 mU mL-1 in diluted human serum (10%), which offers the capability to exploit ratiometric responses for bioactive substances under practical conditions.